Reservoir and mixer constrained scheduling for sample preparation on digital microfluidic biochips

In recent years, digital microfluidic biochips are being dominantly used for implementing a wide range of biochemical laboratory protocols (bioprotocols) on hand-held devices. Accurate preparation of fluid-samples is a fundamental preprocessing step that is needed in many bioprotocols. Oftentimes, the number of reservoirs built on-chip may be far less than that of the reactant fluids to be mixed. Hence, during the execution of an assay, several fluids are to be unloaded from the reservoirs to make room for loading new fluids stored off-line. Such unload-wash-load steps (switching) may be required several times, and these steps, being manual, significantly impact assay-completion time. In this paper, we propose a new scheduling scheme namely Reservoir and Mixer constrained Scheduling (RMS) that can schedule a mixing tree obtained by a mixing algorithm, while minimizing the number of switching such that the total completion time can be minimized. Simulation results over a large number of target ratios show that given the mixing trees obtained by standard mixing algorithms such as MinMix/RMA/CoDOS, RMS reduces switching steps (on average by 40.3%/41.9%/33%) at the cost of increasing mixing time (by only 3.5%/6.2%/4.8%), compared to an existing scheduling scheme invoked with reservoir constraints.

[1]  Krishnendu Chakrabarty,et al.  Optimization of Dilution and Mixing of Biochemical Samples Using Digital Microfluidic Biochips , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  Philip Brisk,et al.  Path scheduling on digital microfluidic biochips , 2012, DAC Design Automation Conference 2012.

[3]  Peter Bartenstein,et al.  A solvent resistant lab-on-chip platform for radiochemistry applications. , 2014, Lab on a chip.

[4]  Lingzhi Luo,et al.  Optimal scheduling of biochemical analyses on digital microfluidic systems , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Fei Su,et al.  Architectural-level synthesis of digital microfluidics-based biochips , 2004, ICCAD 2004.

[6]  Krishnendu Chakrabarty,et al.  Theory and analysis of generalized mixing and dilution of biochemical fluids using digital microfluidic biochips , 2014, ACM J. Emerg. Technol. Comput. Syst..

[7]  Chia-Hung Liu,et al.  Sample preparation for many-reactant bioassay on DMFBs using common dilution operation sharing , 2013, 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[8]  Krishnendu Chakrabarty,et al.  A Reagent-Saving Mixing Algorithm for Preparing Multiple-Target Biochemical Samples Using Digital Microfluidics , 2012, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[9]  Fei Su,et al.  Digital Microfluidic Biochips - Synthesis, Testing, and Reconfiguration Techniques , 2006 .

[10]  Krishnendu Chakrabarty,et al.  Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips , 2015, TODE.

[11]  William Thies,et al.  Abstraction layers for scalable microfluidic biocomputing , 2008, Natural Computing.

[12]  C. Simmons,et al.  A digital microfluidic platform for primary cell culture and analysis. , 2012, Lab on a chip.

[13]  S. Kumar,et al.  Efficient mixture preparation on digital microfluidic biochips , 2013, 2013 IEEE 16th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS).

[14]  Jos Jonkers,et al.  A high-throughput splinkerette-PCR method for the isolation and sequencing of retroviral insertion sites , 2009, Nature Protocols.

[15]  Phil Paik,et al.  Electrowetting-based droplet mixers for microfluidic systems. , 2003, Lab on a chip.